Several works set a goal, like ours, of understanding the model itself, rather than its decisions. Some works (e.g [1]}) try to assign a specific concept to each dimension. Other methods
[2]}, [3]}, [4]}, [5]}, [6]}
also try to infer the meaning of specific dimensions or layers and also to understand
the semantic of internal elements of the network.
A recent work [7]} tries to understand sentence similarity by creating AMR graphs of the input sentences, mapping sub-embeddings into
semantic aspects of the AMRs and use AMR metrics to compute similarity.
